Structural MonitoringPublished result
Infrastructure displacement monitoring
A low-cost marker pipeline for 6-DoF pose and displacement measurement, characterized against controlled reference motion and applied to structural experiments.
Arman Neyestani, PhD
Computer Vision Engineer & Researcher
Vision systems built to measure the physical world.
I build and validate camera-based systems for structural inspection and displacement measurement, using calibration and reference data to connect image output with physical motion. My other work includes visual localization and environmental sensing.
Research Fellow · CeSMA, University of Naples Federico II
Camera geometry
Machine perception
Reference validation
Uncertainty
Selected work
Built, measured, validated.
Published studies, open datasets, and repository outputs are labelled with results, methods and clear limits, starting with my structural measurement work.
Structural MonitoringRepository output
Crack verification
A shared ResNet-101 encoder learns a 128-dimensional embedding for deciding whether two images show the same crack pattern.
Structural MonitoringPublished result
Crack segmentation
Pixel-level crack masks for civil-infrastructure inspection, developed around a YOLOv8 segmentation workflow and UAV-acquired imagery.
Visual LocalizationDataset + benchmark
SUBVO
An open, calibrated underwater sequence with measured ground truth and a benchmark-oriented monocular visual-odometry pipeline.
Visual LocalizationPublished result
UAV visual localization
Monocular visual and visual-inertial localization studies that propagate feature, camera, attitude, and altitude uncertainty into UAV position estimates.
Core technical stack
Computer vision from pixels to physical units.
Tools and methods I have applied across published studies and research prototypes.
- Python
- OpenCV
- PyTorch
- TensorFlow / Keras
- YOLO detection & segmentation
- Metric learning
- Camera calibration
- PnP & 6-DoF pose
- Visual odometry & localization
- Uncertainty analysis
- Jetson
- Raspberry Pi
More computer-vision work
Marine, environmental and applied projects.
Applied Vision · 2023
MAR Rover
A four-class segmentation prototype converts orchard imagery into a visible soil corridor and image-space guidance signal for an amphibious rover.
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Applied Vision · 2025
Blue crab measurement
Instance masks, aligned depth, and camera calibration are combined to estimate projected crab dimensions in millimeters.
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Environmental Sensing · 2024
Wave-height forecasting
A GRU model turns 16-step marine observations into a one-step-ahead significant-wave-height estimate for digital-twin prototypes.
↗Working method
From pixels to physical evidence.
01 Frame the measurement problem.
02 Build the perception and geometry pipeline.
03 Compare against reference data.
04 Report uncertainty and failure modes.
Selected publications
Peer-reviewed context.
Vision-Based Displacement Measurement for Structural Health Monitoring: A Metrology-Oriented Review of Uncertainty Quantification
Buildings 16(13), 2659
Development and Evaluation of a Novel Marker-Based Tracking System for 3D-Scaled Masonry Models Using DeepTag
XXXIII International Scientific Conference Electronics (ET)
SUBVO Dataset: Analyzing Feature Extraction for Underwater Monocular Visual Odometry
IEEE I2MTC
A Low-Cost Vision-Based Monitoring System for Dynamic Testing of a Cantilever Beam
IEEE MetroSustainability
Open to the right problem